1. Field of the Invention
Embodiments of the invention provide a tracker component for a behavioral recognition system. More specifically, embodiments of the invention relate to techniques for identifying objects depicted in a sequence of video frames and for generating, matching, and updating computational models of such objects
2. Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, some currently available systems are configured to identify and track objects moving within a sequence of video frame using a frame-by-frame analysis. These systems typically acknowledge the need to isolate foreground images from background images (i.e., for identifying portions of a scene that depict activity (e.g., people, vehicles, etc.) and portions that depict fixed elements of the scene (e.g., a road or a subway platform). The background essentially provides a stage upon which activity occurs. Contiguous regions of the scene that contain a portion of scene foreground (referred to as a foreground “blob”) are identified, and a given “blob” may be matched from frame-to-frame as depicting the same object. That is, the “blob” is tracked as it moves from frame-to-frame within the scene. To be effective, a video surveillance system needs to be able to detect and follow objects within a scene in a quick and efficient manner as the video is captured at a real-time frame rate. Once identified, a “blob” may be tracked from frame-to-frame in order to follow the movement of the “blob” over time, e.g., a person walking across the field of vision of a video surveillance camera.
Further, such systems may be able to determine when an object has engaged in certain predefined behaviors. However, such surveillance systems typically require that the objects and/or behaviors which may be recognized by the system to be defined in advance. Thus, in practice, these systems simply compare recorded video to predefined definitions for objects and/or behaviors. In other words, unless the underlying system includes a description of a particular object or behavior, the system may not recognize that behavior (or at least instances of the pattern describing the particular object or behavior). Thus, to recognize additional objects or behaviors, separate software products may need to be developed. This results in surveillance systems with recognition capabilities that are labor intensive and prohibitively costly to maintain or adapt for different specialized applications. Further, such systems are often unable to associate related aspects from different patterns of observed behavior, e.g., to learn to identify behavior being repeatedly performed by a criminal prior to breaking into cars parked in a parking lot. As a result, by restricting the range of objects that a system may recognize using a predefined set of patterns, many available video surveillance systems have been of limited usefulness.